1. Technical Field
The present invention relates to metric learning and more specifically to large-scale strongly supervised ensemble metric learning.
2. Description of the Related Art
The goal of metric learning is to find appropriate similarity measurements between pairs of instances that preserve a desired distance structure. Recently, many supervised metric learning methods have been proposed to learn Mahanalobis distance metrics for clustering or k-nearest neighbor classification. Supervised metric learning can be divided into two categories based upon supervision type. Weakly supervised metric learning learns metrics from directly provided pairwise constraints between instances. Such weak constrains are also known as side information. Strongly supervised metric learning receives explicit class labels assigned to every instance from which a large number of constraints can be generated. While conventional metric learning methods perform well in data sets with a smaller number of features, they are very limited in tasks with high dimensional data. This is particularly true when using overcomplete representations of data, where high amounts of redundancy need to be carefully addressed.